Law, T. and Shawe-Taylor, J. (2017) Practical Bayesian Support Vector Regression for Financial Time Series Prediction and Market Condition Change Detection. Quantitative Finance, 17, 1403-1416.
https://doi.org/10.1080/14697688.2016.1267868

has been cited by the following article:

• TITLE: A Bayesian Regression Model and Applications

  AUTHORS: Yijun Yu

  KEYWORDS: Bayesian, Regression, Applications

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.8 No.9, September 22, 2020

  ABSTRACT: A sparse vector regression model is developed. The model is established by employing Bayesian formulation and trained by using a set of data . The parameters needed to be determined in the algorithm are reduced by a special prior hyperparameter setting, and therefore the algorithm is simpler than similar type of Bayesian vector regression models. The examples of applications to the function approximation and inverse scattering problem are presented.